Getting Started in ‘Big Data’

Wanted: Ph.D.-level statistician with the technical skill to use data-visualization software and a deep understanding of the _____ industry.

Fill in the blank with almost any business: consumer products, entertainment, health care, semiconductors or fast food. The list reflects the growing range of companies trying to mine mountains of data in hopes of improving product design, supply chains, customer service or other operations.

Netflix uses data analysis to refine movie recommendations and customer searches, as well as to identify which movies and TV shows to license or develop. The DVD and video-streaming service added more than 2.3 million subscribers in the fourth quarter, helped by the popularity of films like “Skyfall” and “The Iron Lady,” and the pace shows little sign of slacking.

“The key to big data is whether it’s going to give you actionable insights that you can then grow your business on,” said Xavier Amatriain, Netflix’s director of algorithms engineering.

At the most basic level, big data is the art and science of collecting and combing through vast amounts of information for insights that aren’t apparent on a smaller scale. Financial executives who want to harness big data face a critical hurdle: Finding people who can glean it, understand it, and translate it into plain English.

The field is so new that the U.S. Bureau of Labor Statistics doesn’t yet have a classification for data scientists, according to BLS economist Sara Royster. That makes it tough to estimate the unemployment rate or salaries for job seekers in the field.

But executives and recruiters, who compete for talent in the nascent specialty, point to hiring strategies that can get a big-data operation off the ground. They say they look for specific industry experience, poach from data-rich rivals, rely on interview questions that screen out weaker candidates and recommend starting with small projects.

David Ginsberg, chief data scientist at business-software maker SAP AG , said communication skills are critically important in the field, and that a key player on his big-data team is a “guy who can translate Ph.D. to English. Those are the hardest people to find.”

Along with the ability to explain their findings, data scientists need to have a proven record of being able to pluck useful information from data that often lack an obvious structure and may even come from a dubious source. This expertise doesn’t always cut across industry lines. A scientist with a keen knowledge of the entertainment industry, for example, won’t necessarily be able to transfer his skills to the fast-food market.

Some candidates can make the leap. Wolters Kluwer NV, a Netherlands-based information-services provider, has had some success in filling big-data jobs by recruiting from other, data-rich industries, such as financial services. “We have found tremendous success with going to alternative sources and looking at different businesses and saying, ‘What can you bring into our business?’ ” said Kevin Entricken, the company’s chief financial officer.

The trick, some experts say, is finding a candidate steeped in higher mathematics with hands-on familiarity with a particular business. “When you have all those Ph.D.s in a room, magic doesn’t necessarily happen because they may not have the business capability,” said Andy Rusnak, a senior executive for the Americas in Ernst & Young’s advisory practice.

Companies can hamstring themselves in big-data projects by thinking too long term, Mr. Rusnak said. They should focus instead on what they can discover in an eight- to 10-week period, he said, and think less about business transformation.

Dunkin’ Brands Group Inc. aims to wring all the value it can out of its data, by using it to entice customers to visit its stores more often and try new doughnuts and drinks. Last week, it went national with a loyalty program that will allow it to harvest data on customer habits.

The program allows the company to target individuals who opt into the program with specific offers aimed at making them more frequent customers. “If you’ve only been coming in the morning, perhaps we’d give you an offer for the afternoon,” said Dunkin’ Chief Information Officer Jack Clare.

Netflix’s Mr. Amatriain said, “I like to face candidates with real practical problems.” He said he will say to an applicant, “You have this data that comes from our users. How can you use it to solve this particular problem? How would you turn it into an algorithm that would recommend movies?” He said that the question is deliberately open-ended, forcing candidates to prove that they can understand not only the math, but what he calls “the big picture approach to using big data to gain insights.”

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